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作者机构:Sorbonne Univ INSERM UM1127 Inst Cerveau & Moelle Epiniere F-75013 Paris France Univ Estadual Paulista ICTP South Amer Inst Fundamental Res IFT BR-01140070 Sao Paulo Brazil Hop La Pitie Salpetriere CNRS UMR7225 F-75013 Paris France
出 版 物:《CHAOS》 (混沌;多学科非线性学杂志)
年 卷 期:2018年第28卷第12期
页 面:123111-123111页
核心收录:
学科分类:07[理学] 0701[理学-数学] 0702[理学-物理学] 070101[理学-基础数学]
基 金:Sao Paulo Research Foundation (FAPESP) [2016/01343-7, 2017/00344-2] Swedish Research Council [2017-00344] Funding Source: Swedish Research Council
主 题:TIME series analysis NONLINEAR analysis LINEAR statistical models DISTRIBUTION (Probability theory) DATA analysis
摘 要:Time irreversibility is a common signature of nonlinear processes and a fundamental property of non-equilibrium systems driven by non-conservative forces. A time series is said to be reversible if its statistical properties are invariant regardless of the direction of time. Here, we propose the Time Reversibility from Ordinal Patterns method (TiROP) to assess time-reversibility from an observed finite time series. TiROP captures the information of scalar observations in time forward as well as its time-reversed counterpart by means of ordinal patterns. The method compares both underlying information contents by quantifying its (dis)-similarity via the Jensen-Shannon divergence. The statistic is contrasted with a population of divergences coming from a set of surrogates to unveil the temporal nature and its involved time scales. We tested TiROP in different synthetic and real, linear, and non-linear time series, juxtaposed with results from the classical Ramsey s time reversibility test. Our results depict a novel, fast-computation, and fully data-driven methodology to assess time-reversibility with no further assumptions over data. This approach adds new insights into the current non-linear analysis techniques and also could shed light on determining new physiological biomarkers of high reliability and computational efficiency. Published by AIP Publishing.